The Best Machine Theory eBooks of All Time

Discover the most influential machine theory ebooks, recommended by leaders, experts, and readers worldwide

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Including recommendations by Bernhard Scholkopf, Avrim Blum and Peter Bartlett

Not sure what to read? Our AI can suggest the most recommended Machine Theory books!

1
Book Cover of Shai Shalev-Shwartz, Shai Ben-David - Understanding Machine Learning: From Theory to Algorithms

By Shai Shalev-Shwartz – Associate Professor at Hebrew University of Jerusalem (you?) and 1 more 

4.65
| 2014 | 410 Pages
Recommended for: 
Advanced undergraduate and graduate students. Ages 12 to Adults.
You will:
  • Learn the fundamental principles of machine learning and their applications in various fields.
  • Discover the mathematical foundations that underpin machine learning algorithms and techniques.
  • Understand the importance of computational complexity in the context of learning algorithms.
  • Explore various algorithmic paradigms including neural networks and stochastic gradient descent.
  • Gain insights into emerging theoretical concepts such as PAC-Bayes approach and compression-based bounds.
Reviews:
Clear Introduction
Well-Structured
Cohesive Writing
Theoretical Background
Practical Algorithms
Heavy on Math
Poor Print Quality
  • #37 Best Seller in Computer Vision & Pattern Recognition on Amazon
  • New York Times Bestseller
  • Rated Amazon Best Book of the Year
Bernhard ScholkopfThis elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data
Avrim BlumThis is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field
Peter BartlettThis text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course
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2
Book Cover of Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

By Mehryar Mohri – Professor of Computer Science at NYU (you?) and 2 more 

4.55
| 2018 | 504 Pages
Recommended for: 
Graduate students and researchers in machine learning. Ages 12 to Adults.
You will:
  • Learn the theoretical foundations of machine learning algorithms and their applications.
  • Discover the Probably Approximately Correct (PAC) learning framework and its implications.
  • Understand generalization bounds based on Rademacher complexity and VC-dimension.
  • Explore advanced topics like kernel methods, boosting, and reinforcement learning.
  • Gain insights into model selection and information theory through new chapters.
Reviews:
Theoretical Depth
Comprehensive Coverage
Rigorous Approach
Well-Structured
Challenging Exercises
Heavy Formalism
Lacks Practical Examples
  • New York Times Bestseller
  • Rated Amazon Best Book of the Year
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Machine Theory Book made by AI

By TailoredRead – AI that creates personalized books for you 

4.98
| 2025 | 30-300 pages
Learn Machine Theory faster with a book created specifically for you by state-of-the-art AI. Our AI has vast knowledge of Machine Theory, and will craft a custom-tailored book for you in just 10 minutes. This tailored book addresses YOUR unique interests, goals, knowledge level, and background. Available for online reading, PDF download, and Kindle, your custom book will provide personalized insights to help you learn faster, expand your horizons, and accomplish your goals. Embark on your Machine Theory learning journey with a personalized book - made exclusively for you.
Recommended for: 
All readers across all knowledge levels.
You will:
  • Get a Machine Theory book tailored to your interests, goals, and background
  • Receive a book precisely matching your background and level of knowledge
  • Select which topics you want to learn, exclude the topics you don't
  • Define your learning goals and let your book guide you to accomplish them
  • Get all the knowledge you need consolidated into a single focused book
Reviews:
Insightful
Focused
Highly Personalized
Easy to Read
Engaging
Actionable
Up-to-Date
3
Book Cover of RODRIGO F MELLO, Moacir Antonelli Ponti - Machine Learning: A Practical Approach on the Statistical Learning Theory

By RODRIGO F MELLO – Associate Professor, University of São Paulo (you?) and 1 more 

4.40
| 2018 | 377 Pages
Recommended for: 
Students and self-learners in data science. Ages 12 to Adults.
You will:
  • Learn fundamental concepts of Statistical Learning Theory through practical examples and algorithms.
  • Discover how to implement classification algorithms using R scripts provided in the book.
  • Understand the Bias-Variance Dilemma and its significance in machine learning.
  • Explore optimization concepts related to Support Vector Machines and their applications.
  • Gain insights into the motivations for studying data spaces and improving classification results.
Reviews:
Practical Examples
Easy to Understand
Theoretical Depth
Well Illustrated
Hands-on Tour
Lots of Typos
Not Informative
  • New York Times Bestseller
  • Rated Amazon Best Book of the Year
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